Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Sequence Networks of Rotating Machines

Sequence Networks of Rotating Machines

A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Special Issue on Machine Learning and AI for Sensors.

Sensors (Basel, Switzerland)·2023

Same author

A Convolutional Autoencoder Topology for Classification in High-Dimensional Noisy Image Datasets.

Sensors (Basel, Switzerland)·2021

Same author

Explainable Machine Learning Framework for Image Classification Problems: Case Study on Glioma Cancer Prediction.

Journal of imaging·2021

Same author

Smoothing and stationarity enforcement framework for deep learning time-series forecasting.

Neural computing & applications·2021

Same journal

Human-AI Interaction in Interventional Radiology: A Narrative Review of Current Applications, Challenges, and Future Directions.

Journal of imaging·2026

Same journal

Coronary Artery Anomalies and Anatomical Variants: Cross-Sectional Diagnostic Imaging and Clinical Background.

Journal of imaging·2026

Same journal

YoLeTooth: A Unified Framework for Joint Tooth Segmentation and Periapical Lesion Detection in Panoramic Radiographs.

Journal of imaging·2026

Same journal

Radiomics-Guided Multi-Sequence Learning for Pathological Complete Response Prediction from Breast MRI with Missing Auxiliary Sequences.

Journal of imaging·2026

Same journal

Cutaneous Thermography in Arthropathies: Quantitative Imaging, Machine Learning, and Clinical Translation.

Journal of imaging·2026

Same journal

Two-Stage Dynamic Synergistic Segmentation Method for Myocardial Pathology.

Journal of imaging·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 12, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Explainable Image Similarity: Integrating Siamese Networks and Grad-CAM.

Ioannis E Livieris¹, Emmanuel Pintelas², Niki Kiriakidou³

¹Department of Statistics & Insurance, University of Piraeus, GR 185-34 Piraeus, Greece.

Journal of Imaging

|October 27, 2023

Summary

This summary is machine-generated.

This study introduces explainable image similarity, offering visual explanations for image comparisons. The new framework enhances trust and understanding in image-based AI systems.

Keywords:

Grad-CAM explainability recommendations siamese networks

More Related Videos

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Related Experiment Videos

Last Updated: Jul 12, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

The increasing use of image-based applications necessitates accurate and interpretable image similarity measures.
Current models often lack transparency, hindering understanding of similarity judgments.

Purpose of the Study:

To develop an explainable image similarity approach providing both scores and visual explanations.
To enhance the interpretability and trustworthiness of image-based systems.

Main Methods:

Integration of Siamese Networks for feature extraction.
Application of Gradient-weighted Class Activation Mapping (Grad-CAM) for visual explanations.
Development of a framework for generating factual and counterfactual explanations.

Main Results:

The proposed framework successfully generates similarity scores with accompanying visual explanations.
Demonstrated potential for providing factual and counterfactual insights into image similarity.
The approach facilitates better decision-making by clarifying similarity reasoning.

Conclusions:

Explainable image similarity enhances interpretability, trustworthiness, and user acceptance.
The framework offers a novel method for understanding image comparisons in AI.
Addresses the critical need for transparency in image-based AI applications.